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Abstract

Weight is an important indicator of current and future health and it is more crucial in children who are tomorrow’s adults. This research analyzes physical activity, food environment and socioeconomic factors but recognizes that there may be other factors, not included in the analyses that are influencing overweight and obesity. Contrary to the conventional thinking of global analysis, this research suggests localized analysis and need-based interventions. The one-size-fit-all strategy may not be effective in controlling obesity rates since each neighbourhood or set of neighbourhoods has unique characteristics that need to be addressed individually. This thesis offers an innovative framework combining local analysis with simulation modeling to analyze childhood overweight and obesity for children 4.5 to 6 years old. Spatial models generally do not deal with simulation over time, making it cumbersome for health planners and policy makers to effectively design and implement interventions and to quantify their impact over time. This research fills this gap by combining geographically weighted regression (GWR) to identify vulnerable neighbourhoods and critical factors for childhood overweight and obesity, and simulation modeling to evaluate the impact of suggested interventions on the targeted neighbourhoods. Walkability was chosen as a potential intervention to test the framework. Simulation results suggest that some walkability interventions would achieve measurable declines in childhood obesity rates. The result appears encouraging, and the improvement will likely compound over time. Moreover, the significant association between obesity and walkability decreases over time, exposing other factors that can be targeted at a later stage. The research further addresses an outstanding issue in the emerging GWR method, local multicollinearity, by proposing a novel solution. Another contribution in the GWR and cartography literature is the introduction of an innovative way of mapping t-values and R2. Overall, the results demonstrate that the integration of GWR and simulation modeling is cost effective, flexible and general in nature that can be applied to different areas and to address other health issues. The innovative framework has great potential for health professionals and policy makers to design obesity control and prevention programs that meet the unique characteristics of each neighbourhood.